Identity Recognition Demo.¶
An attempt to recognize a person underneath a face mask. This project is trained on a single team member (Hieu) as if a system is using his biometrics for login. Therefore it is meant to only recognize one person at a time. However you can modify and play around with the code
The main focus on this project is try to recognize the person underneath a mask. We have trained a full face model on one of our team members (Hieu) for traditional whole face recognition however there are more accurate algorithms out there that you can use for traditional face recognition
- The notebook will output a probability of a person once finished running
This folder contains a couple images that you can use:
- hieumask.jpg
- deanmask.jpg
- madmask.jpg
- hewanmask.jpg
You can modify the filepaths below to change it or even upload your own photos to the folder to test. This project also assumes that you've uploaded this demo onto the top root level of Google Drive; It's meant to be ran under Colaboratory
- When using with Colaboratory, please enable GPU
Intial Setup¶
In [1]:
from google.colab import drive
drive.mount('/content/drive')
In [2]:
#Describe input. You will also need to upload the testImage
inputImagePath = ""
decision = 0
if(decision == 0):
inputImagePath = "/content/drive/MyDrive/IdentityRecognitionDemov5/hieumask.jpg"
#inputImagePath = "/content/drive/MyDrive/IdentityRecognitionTestFiles/testImage6.jpg"
if(decision == 1):
inputImagePath = "/content/drive/MyDrive/IdentityRecognitionDemov5/deanmask.jpg"
if(decision == 2):
inputImagePath = "/content/drive/MyDrive/IdentityRecognitionDemov5/madmask.jpg"
if(decision == 3):
inputImagePath = "/content/drive/MyDrive/IdentityRecognitionDemov5/hewanmask.jpg"
In [ ]:
print(inputImagePath)
In [ ]:
!git clone https://github.com/ultralytics/yolov5.git
%cd yolov5/
!pip install -r requirements.txt >/dev/null
In [5]:
import cv2, os
import numpy as np
import matplotlib.pyplot as plt
import math, decimal, copy, glob
#from PIL import Image as im
import torch
import glob
from IPython.display import Image, display
import pandas as pd
import json
Is there a mask?¶
In [ ]:
!python detect.py --source {inputImagePath} --weights /content/drive/MyDrive/IdentityRecognitionDemov5/maskweight.pt --img 416 --save-txt --save-conf
maskExists = False
maskExistsConfidence = float(0)
labelDetected = 0
In [7]:
labelPath = os.listdir("/content/yolov5/runs/detect/exp/labels")
labelPath = labelPath[0]
with open("/content/yolov5/runs/detect/exp/labels/" + labelPath) as f:
lines = f.read()
maskConfidence = lines.split()
maskExistsConfidence = float(maskConfidence[5])
labelDetected = int(maskConfidence[0])
if(labelDetected == 0):
print("There's a mask!")
maskExists = True
else:
print("There's no mask!")
for imageName in glob.glob('/content/yolov5/runs/detect/exp/*.jpg'): #assume JPG file
display(Image(filename=imageName))
#print("\n")
If there's a mask...¶
Confidence from half face¶
In [8]:
def detectHalfFace():
halfFaceWeightPath = "/content/drive/MyDrive/IdentityRecognitionDemov5/tophalffaceweight.pt"
trainedHalfFaceModel = torch.hub.load('/content/yolov5', 'custom', path=halfFaceWeightPath, source='local') # local repo
halfFaceResults = trainedHalfFaceModel(inputImagePath)
halfFaceResults.save()
for imageName in glob.glob('/content/yolov5/runs/detect/exp2/*.jpg'): #assume JPG file
display(Image(filename=imageName))
#print("\n")
halfFaceResultsJSON = json.loads(halfFaceResults.pandas().xyxy[0].to_json(orient="records") )
return float(halfFaceResultsJSON[0]['confidence'])
Confidence from eye area¶
In [9]:
def detectEyeArea():
eyeAreaWeightPath = "/content/drive/MyDrive/IdentityRecognitionDemov5/eyeareaweight.pt"
trainedEyeAreaModel = torch.hub.load('/content/yolov5', 'custom', path=eyeAreaWeightPath, source='local') # local repo
eyeAreaResults = trainedEyeAreaModel(inputImagePath)
eyeAreaResults.save()
for imageName in glob.glob('/content/yolov5/runs/detect/exp3/*.jpg'): #assume JPG file
display(Image(filename=imageName))
#print("\n")
eyeAreaResultsJSON = json.loads(eyeAreaResults.pandas().xyxy[0].to_json(orient="records") )
return float(eyeAreaResultsJSON[0]['confidence'])
Confidence probability¶
In [10]:
probability = float(0)
if(maskExists):
probability = round((float(detectHalfFace() + detectEyeArea())/2) * 100, 2)
If there's no mask¶
In [11]:
def detectWholeFace():
wholeFaceWeightPath = "/content/drive/MyDrive/IdentityRecognitionDemov5/wholefaceweight.pt"
trainedWholeFaceModel = torch.hub.load('/content/yolov5', 'custom', path=wholeFaceWeightPath, source='local') # local repo
wholeFaceResults = trainedWholeFaceModel(inputImagePath)
wholeFaceResults.save()
for imageName in glob.glob('/content/yolov5/runs/detect/exp/*.jpg'): #assume JPG file
display(Image(filename=imageName))
#print("\n")
wholeFaceResultsJSON = json.loads(wholeFaceResults.pandas().xyxy[0].to_json(orient="records") )
return float(wholeFaceResultsJSON[0]['confidence'])
In [12]:
if(not maskExists):
!python detect.py --source {inputImagePath} --weights /content/drive/MyDrive/IdentityRecognitionDemov5/wholefaceweight.pt --img 416 --save-txt --save-conf
labelPath = os.listdir("/content/yolov5/runs/detect/exp2/labels")
labelPath = labelPath[0]
with open("/content/yolov5/runs/detect/exp2/labels/" + labelPath) as f:
lines = f.read()
noMaskConfidenceRead = lines.split()
noMaskConfidence = float(noMaskConfidenceRead[5])
for imageName in glob.glob('/content/yolov5/runs/detect/exp2/*.jpg'): #assume JPG file
display(Image(filename=imageName))
#print("\n")
probability = round(noMaskConfidence * 100, 2)
Result¶
In [13]:
from google.colab.patches import cv2_imshow
print(f"This has the probability of {probability}% of being the user")
#display(Image(filename=InputImagePath))
image = cv2.imread(inputImagePath)
cv2.putText(image, f"This has the probability of {probability}% of being the user", org = (50,50), fontFace=cv2.FONT_HERSHEY_SIMPLEX , fontScale=2, color=(255, 0, 0),thickness=4)
cv2_imshow(image)
print(f"This has the probability of {probability}% of being the user")
